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  <h1>Source code for torch.autograd</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">``torch.autograd`` provides classes and functions implementing automatic</span>
<span class="sd">differentiation of arbitrary scalar valued functions. It requires minimal</span>
<span class="sd">changes to the existing code - you only need to declare :class:`Tensor` s</span>
<span class="sd">for which gradients should be computed with the ``requires_grad=True`` keyword.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">warnings</span>

<span class="kn">from</span> <span class="nn">.variable</span> <span class="kn">import</span> <span class="n">Variable</span>
<span class="kn">from</span> <span class="nn">.function</span> <span class="kn">import</span> <span class="n">Function</span><span class="p">,</span> <span class="n">NestedIOFunction</span>
<span class="kn">from</span> <span class="nn">.gradcheck</span> <span class="kn">import</span> <span class="n">gradcheck</span><span class="p">,</span> <span class="n">gradgradcheck</span>
<span class="kn">from</span> <span class="nn">.grad_mode</span> <span class="kn">import</span> <span class="n">no_grad</span><span class="p">,</span> <span class="n">enable_grad</span><span class="p">,</span> <span class="n">set_grad_enabled</span>
<span class="kn">from</span> <span class="nn">.anomaly_mode</span> <span class="kn">import</span> <span class="n">detect_anomaly</span><span class="p">,</span> <span class="n">set_detect_anomaly</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">profiler</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">functional</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Variable&#39;</span><span class="p">,</span> <span class="s1">&#39;Function&#39;</span><span class="p">,</span> <span class="s1">&#39;backward&#39;</span><span class="p">,</span> <span class="s1">&#39;grad_mode&#39;</span><span class="p">]</span>


<span class="k">def</span> <span class="nf">_make_grads</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span>
    <span class="n">new_grads</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">out</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Mismatch in shape: grad_output[&quot;</span>
                                   <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grads</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">grad</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;] has a shape of &quot;</span>
                                   <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; and output[&quot;</span>
                                   <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">out</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;] has a shape of &quot;</span>
                                   <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span><span class="p">)</span>
            <span class="n">new_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
                <span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;grad can be implicitly created only for scalar outputs&quot;</span><span class="p">)</span>
                <span class="n">new_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">preserve_format</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">new_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;gradients can be either Tensors or None, but got &quot;</span> <span class="o">+</span>
                            <span class="nb">type</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">new_grads</span><span class="p">)</span>


<div class="viewcode-block" id="backward"><a class="viewcode-back" href="../../autograd.html#torch.autograd.backward">[docs]</a><span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">grad_tensors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">grad_variables</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes the sum of gradients of given tensors w.r.t. graph leaves.</span>

<span class="sd">    The graph is differentiated using the chain rule. If any of ``tensors``</span>
<span class="sd">    are non-scalar (i.e. their data has more than one element) and require</span>
<span class="sd">    gradient, then the Jacobian-vector product would be computed, in this</span>
<span class="sd">    case the function additionally requires specifying ``grad_tensors``.</span>
<span class="sd">    It should be a sequence of matching length, that contains the &quot;vector&quot;</span>
<span class="sd">    in the Jacobian-vector product, usually the gradient of the differentiated</span>
<span class="sd">    function w.r.t. corresponding tensors (``None`` is an acceptable value for</span>
<span class="sd">    all tensors that don&#39;t need gradient tensors).</span>

<span class="sd">    This function accumulates gradients in the leaves - you might need to zero</span>
<span class="sd">    them before calling it.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        tensors (sequence of Tensor): Tensors of which the derivative will be</span>
<span class="sd">            computed.</span>
<span class="sd">        grad_tensors (sequence of (Tensor or None)): The &quot;vector&quot; in the Jacobian-vector</span>
<span class="sd">            product, usually gradients w.r.t. each element of corresponding tensors.</span>
<span class="sd">            None values can be specified for scalar Tensors or ones that don&#39;t require</span>
<span class="sd">            grad. If a None value would be acceptable for all grad_tensors, then this</span>
<span class="sd">            argument is optional.</span>
<span class="sd">        retain_graph (bool, optional): If ``False``, the graph used to compute the grad</span>
<span class="sd">            will be freed. Note that in nearly all cases setting this option to ``True``</span>
<span class="sd">            is not needed and often can be worked around in a much more efficient</span>
<span class="sd">            way. Defaults to the value of ``create_graph``.</span>
<span class="sd">        create_graph (bool, optional): If ``True``, graph of the derivative will</span>
<span class="sd">            be constructed, allowing to compute higher order derivative products.</span>
<span class="sd">            Defaults to ``False``.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">grad_variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;&#39;grad_variables&#39; is deprecated. Use &#39;grad_tensors&#39; instead.&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">grad_tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">grad_tensors</span> <span class="o">=</span> <span class="n">grad_variables</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;&#39;grad_tensors&#39; and &#39;grad_variables&#39; (deprecated) &quot;</span>
                               <span class="s2">&quot;arguments both passed to backward(). Please only &quot;</span>
                               <span class="s2">&quot;use &#39;grad_tensors&#39;.&quot;</span><span class="p">)</span>

    <span class="n">tensors</span> <span class="o">=</span> <span class="p">(</span><span class="n">tensors</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">grad_tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">grad_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_tensors</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
        <span class="n">grad_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">grad_tensors</span><span class="p">]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">grad_tensors</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">grad_tensors</span><span class="p">)</span>

    <span class="n">grad_tensors</span> <span class="o">=</span> <span class="n">_make_grads</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">grad_tensors</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">retain_graph</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">retain_graph</span> <span class="o">=</span> <span class="n">create_graph</span>

    <span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">run_backward</span><span class="p">(</span>
        <span class="n">tensors</span><span class="p">,</span> <span class="n">grad_tensors</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span>
        <span class="n">allow_unreachable</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># allow_unreachable flag</span></div>


<div class="viewcode-block" id="grad"><a class="viewcode-back" href="../../autograd.html#torch.autograd.grad">[docs]</a><span class="k">def</span> <span class="nf">grad</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">grad_outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
         <span class="n">only_inputs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">allow_unused</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes and returns the sum of gradients of outputs w.r.t. the inputs.</span>

<span class="sd">    ``grad_outputs`` should be a sequence of length matching ``output``</span>
<span class="sd">    containing the &quot;vector&quot; in Jacobian-vector product, usually the pre-computed</span>
<span class="sd">    gradients w.r.t. each of the outputs. If an output doesn&#39;t require_grad,</span>
<span class="sd">    then the gradient can be ``None``).</span>

<span class="sd">    If ``only_inputs`` is ``True``, the function will only return a list of gradients</span>
<span class="sd">    w.r.t the specified inputs. If it&#39;s ``False``, then gradient w.r.t. all remaining</span>
<span class="sd">    leaves will still be computed, and will be accumulated into their ``.grad``</span>
<span class="sd">    attribute.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        outputs (sequence of Tensor): outputs of the differentiated function.</span>
<span class="sd">        inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be</span>
<span class="sd">            returned (and not accumulated into ``.grad``).</span>
<span class="sd">        grad_outputs (sequence of Tensor): The &quot;vector&quot; in the Jacobian-vector product.</span>
<span class="sd">            Usually gradients w.r.t. each output. None values can be specified for scalar</span>
<span class="sd">            Tensors or ones that don&#39;t require grad. If a None value would be acceptable</span>
<span class="sd">            for all grad_tensors, then this argument is optional. Default: None.</span>
<span class="sd">        retain_graph (bool, optional): If ``False``, the graph used to compute the grad</span>
<span class="sd">            will be freed. Note that in nearly all cases setting this option to ``True``</span>
<span class="sd">            is not needed and often can be worked around in a much more efficient</span>
<span class="sd">            way. Defaults to the value of ``create_graph``.</span>
<span class="sd">        create_graph (bool, optional): If ``True``, graph of the derivative will</span>
<span class="sd">            be constructed, allowing to compute higher order derivative products.</span>
<span class="sd">            Default: ``False``.</span>
<span class="sd">        allow_unused (bool, optional): If ``False``, specifying inputs that were not</span>
<span class="sd">            used when computing outputs (and therefore their grad is always zero)</span>
<span class="sd">            is an error. Defaults to ``False``.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">only_inputs</span><span class="p">:</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;only_inputs argument is deprecated and is ignored now &quot;</span>
                      <span class="s2">&quot;(defaults to True). To accumulate gradient for other &quot;</span>
                      <span class="s2">&quot;parts of the graph, please use torch.autograd.backward.&quot;</span><span class="p">)</span>

    <span class="n">outputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">outputs</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
    <span class="n">inputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">grad_outputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">grad_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_outputs</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
        <span class="n">grad_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">grad_outputs</span><span class="p">]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">grad_outputs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">grad_outputs</span><span class="p">)</span>

    <span class="n">grad_outputs</span> <span class="o">=</span> <span class="n">_make_grads</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">grad_outputs</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">retain_graph</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">retain_graph</span> <span class="o">=</span> <span class="n">create_graph</span>

    <span class="k">return</span> <span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">run_backward</span><span class="p">(</span>
        <span class="n">outputs</span><span class="p">,</span> <span class="n">grad_outputs</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span>
        <span class="n">inputs</span><span class="p">,</span> <span class="n">allow_unused</span><span class="p">)</span></div>


<span class="c1"># This function applies in case of gradient checkpointing for memory</span>
<span class="c1"># optimization. Currently, for gradient checkpointing, we only support imperative</span>
<span class="c1"># backwards call i.e. torch.autograd.backward() and the torch.autograd.grad() won&#39;t</span>
<span class="c1"># work. The reason being that: torch.autograd.grad() only calculates the grads</span>
<span class="c1"># for the inputs that are passed by user but it doesn&#39;t calculate grad for</span>
<span class="c1"># anything else e.g. model parameters like weights, bias etc. However, for</span>
<span class="c1"># torch.autograd.backward(), we would actually compute the grad for the weights as well.</span>
<span class="c1">#</span>
<span class="c1"># This function returns whether the checkpointing is valid i.e. torch.autograd.backward</span>
<span class="c1"># or not i.e. torch.autograd.grad. The implementation works by maintaining a thread</span>
<span class="c1"># local variable in torch/csrc/autograd/engine.cpp which looks at the NodeTask</span>
<span class="c1"># in the stack and before a NodeTask is executed in evaluate_function, it</span>
<span class="c1"># checks for whether reentrant backwards is imperative or not.</span>
<span class="c1"># See https://github.com/pytorch/pytorch/pull/4594 for more discussion/context</span>
<span class="k">def</span> <span class="nf">_is_checkpoint_valid</span><span class="p">():</span>
    <span class="k">return</span> <span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">is_checkpoint_valid</span><span class="p">()</span>


<span class="k">def</span> <span class="nf">variable</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;torch.autograd.variable(...) is deprecated, use torch.tensor(...) instead&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_autograd_init</span><span class="p">():</span>
    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;autograd initialization failed&quot;</span><span class="p">)</span>
</pre></div>

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